An investigation of the uptake rate of zinc in

a freshwater amphipod (Gammarus pulex) in

binary mixtures with cadmium and copper.

Group number 2

Dominika Jachowicz, dominja@ruc.dk (51476)

Ole Bjørn Brodnicke, obb@ruc.dk (51477)

Aske Hansen, askehan@ruc.dk (51485)

Rasmus Edin Thomasen, edin@ruc.dk (53395)

Supervisor: Farhan Khan

Roskilde University

2nd Semester project, NIB

Natural Science Bachelor

House 14.2

2nd

Semester project

28th

of May 2014

2

Table of Contents

Abstract ................................................................................................................................................ 3

Introduction .......................................................................................................................................... 4

Problem Formulation ....................................................................................................................... 4

Binary mixtures ................................................................................................................................ 4

The Biodynamic Model ................................................................................................................... 7

Water Exposure Method .................................................................................................................. 9

Test species ...................................................................................................................................... 9

Hypothesis ...................................................................................................................................... 10

Method ............................................................................................................................................... 11

Species collection ........................................................................................................................... 11

Preliminary LC50 experiment ........................................................................................................ 11

Uptake experiment ......................................................................................................................... 15

Atomic Absorption Spectroscopy .................................................................................................. 17

Statistical methods ......................................................................................................................... 21

Results ................................................................................................................................................ 22

Mortality......................................................................................................................................... 22

Single uptakes ................................................................................................................................ 23

Binary mixture uptake .................................................................................................................... 26

Statistics ......................................................................................................................................... 29

Linear Regression ...................................................................................................................... 29

One-way ANOVA...................................................................................................................... 33

Discussion .......................................................................................................................................... 35

Mortality......................................................................................................................................... 35

Statistics ......................................................................................................................................... 35

Zinc ................................................................................................................................................ 36

Copper ............................................................................................................................................ 37

Cadmium ........................................................................................................................................ 38

Controls .......................................................................................................................................... 38

Biodynamic model ......................................................................................................................... 38

Bias................................................................................................................................................. 39

Relevance ....................................................................................................................................... 39

Conclusion ......................................................................................................................................... 40

Bibliography....................................................................................................................................... 41

Appendices ......................................................................................................................................... 47

Appendix A: AAS Results ............................................................................................................. 47

Appendix B: Mean Zinc Tissue Burden ........................................................................................ 62

Appendix C: Solution concentrations ............................................................................................ 64

3

Abstract

Polymetal contamination is found throughout the environment. Though studies on single metal

uptake are thorough and complex, taking into account different uptake routes and species, they do

not take into account the effect trace metals may have on each other. It is the aim of this study to

add to this pool of knowledge. The focus of this investigation is zinc, cadmium and copper. To

understand why these trace metals were chosen another important aspect is included: uptake

channels which zinc and cadmium share but copper does not, leading to competitive and non-

competitive uptake. This was investigated by designing and performing an uptake experiment on

the species Gammarus pulex collected from a stream near Østrup. 24 hours mixed trace metal

exposures were carried out to find the effect of cadmium and copper on zinc uptake. Tissue burdens

were measured in the AAS facility at Roskilde University in order to obtain uptake rates. The

results were analyzed using statistical software SPSS to perform an ANOVA, which showed there is

no significant competition between any of the metals in mixture. While this was expected in

mixtures containing copper, due to great variance in the data the result was similar for cadmium.

However, when comparing the slopes of the regression lines, and therefore their uptake rate

constants, it could indicate that zinc competes with cadmium. The uptake rate constant of zinc when

in mixture with cadmium is zero, and therefore it can be seen that cadmium inhibits the

accumulation of zinc. The uptake rate constant of zinc when in mixture with copper showed to be

almost equal to that of zinc alone, suggesting no competition between the two metal ions. The

mortality noted in the experiment is believed to be caused by the overestimation of the copper

LC50, but curiously, high concentrations of zinc seem to have an antagonistic effect on copper

toxicity.

4

Introduction

Trace metal pollution is a wide-spread issue affecting various aquatic ecosystems. Traditionally,

ecotoxicologists focused their research on single-metal exposures and because of this most uptake

and accumulation models take into account only one metal. These tests, however, do not reflect the

conditions in a natural system, where a mixture of multiple metals may be present (Woodward et al.

1995, Farag et al., 1998, Khan et al. 2011, 2012).This fact has brought more attention to binary

mixture research in recent years. Because of this we have decided to conduct a binary trace metal

exposure test in an attempt to obtain more relevant uptake results. Since different metals can

compete for the same uptake routes, the presence of another metal stressor can have a large effect

on the accumulation of the metal (Norwood et al. 2003). We decided to investigate whether the

uptake of zinc is affected by the presence of two other trace metals – cadmium and copper. It should

be noted that henceforth when zinc, copper and cadmium are mentioned this refers to their

waterborne or ionic forms. To investigate this we use an ecologically relevant freshwater amphipod,

Gammarus pulex.

Problem Formulation

Our preliminary literature research and discussion has led us to the following problem formulation:

How does the uptake rate of zinc in a freshwater amphipod change in the presence of copper

or cadmium?

Throughout this report we will answer the problem formulation along with several working

questions which will be stated after the theoretical background.

Binary mixtures

Binary mixtures of trace metals are a field of metal toxicology which has much left to investigate

(Norwood et al., 2003). The main factor investigated concerning binary mixtures is how two metals

affect each other’s uptake or toxicity in a binary mixture and most importantly, can the combined

effect of the two be greater than of one of them by itself. While toxicity refers to “the biological

effect of a substance”, accumulation refers to “the successive additions of a substance to a target

organism, or organ, or to part of the environment, resulting in an increasing amount or

5

concentration of the substance in the organism, organ, or environment” (Duffus et al. 1993).

Trace metals in mixtures may either work competitively, anti-competitively, or have no effect on

each other (non-competitively) (Borgman et al. 2008, Norwood et al. 2003). The first case occurs

often when two metals compete for an uptake transporter or channel such as lead and copper in the

unicellular algae Chlamydomonas reinhardii (Sánchez-Márin et al. 2014) or to use an animal

example ions such as cadmium and zinc competing in the aquatic insect Hydropsyche sparna

(Poteat et al. 2012).

Previous research on mixtures of trace metals includes a study of behavioral and physiological

effects of combined cadmium and arsenate on Gammarus pulex by Vellinger et al, 2012(1).

Ventilatory and locomotion skills were tested as well as ion regulation of sodium and chloride ions

in haemolymph. While the effect on behavioral responses was additive, the presence of arsenate

reduced cadmiums effect on ion regulation. Another study conducted was an accumulation study of

the mollusk Tympanotonus fuscatus L in single and binary mixtures of copper, cadmium and zinc

(Daka et al. 2006). The results show that copper and cadmium accumulate more than additively at

lower concentrations but did compete at higher. They also indicate competition between zinc and

cadmium.

The reason for choosing these metals comes from the conclusion, drawn from literature, that the

uptake of the ions of Zn2+

and Cu2+

happens over different uptake channels in the organism. For

Zn2+

and Cd2+

the uptake would happen over Ca2+

channel, as observed in humans (Kovacs et al.,

2013) and fish (Hogstrand et al. 1991), so competition between the two can be expected, as opposed

to Cu2+

, which has uptake over the Na2+

channel. (Komjarova & Blus, 2009).

Trace metals such as zinc, copper and cadmium are released from byproducts of human industry

and into the environment, ultimately end up in aquatic environments such as lakes, rivers and

streams (Dwivedi & Vankar, 2014). Trace metals are released in ionic form as a byproduct of

agriculture, mining and various other industries (Dwivedi & Vankar, 2014, Tsai et al. 2013, Valko et

al. 2005). Both physiologically essential (such as zinc) and non-essential metals (such as cadmium)

are toxic in high concentrations as they accumulate in the body tissues (Rainbow, 2007), and may

be transported across trophic levels via ingestion. Their toxicity lies mainly in their capability for

catalysis of reactions that would otherwise not take place inside the living organism and in

6

replacing essential elements in important biological molecules. A crucial aspect of trace-metal

toxicity is oxidative stress which is the catalysis of reactions involving reactive oxygen and nitrogen

species within the organism. This can lead to the formation of free radicals, which can in turn cause

cellular-level damage to proteins, lipids and even DNA molecules by producing harmful mutagenic

and carcinogenic substances (Valko et al. 2005). Copper does this by through redox-cycling

reactions while cadmium depletes glutathione and binds to sulfhydryl groups of proteins rendering

them unable to perform their function.

Zinc is considered an essential metal and acute zinc toxicity is rare. Main sources of zinc include

the steel, wood and leather industries (Dwivedi & Vankar, 2014) as well as zinc oxide nanoparticles

(Quingshan et al. 2014). It is mostly a redox inert metal and therefore does not participate in many

redox reactions which could lead to oxidative stress. Although zinc can act as an antioxidant and

reduce cadmium toxicity (Valko et al. 2005) it can also have detrimental effects on the nervous

system when during chronic exposure to excessive concentrations.

Sources of waterborne copper include release of ions from copper nanoparticles and from salts such

as CuSO4 used in aquaculture (Tsai et al. 2013). Copper is also an essential metal in organisms.

Since the most stable oxidation state is Cu2+

, this is the most common one in nature. However, Cu2+

can be reduced to Cu- on a cellular level, which in turn catalyzes the Fenton reaction, causing the

decomposition of hydrogen peroxide into hydroxyl radicals. These can cause cellular-level damage

to different parts of the organism (Valko et al. 2005).

In the environment cadmium originates from Ni-Cd batteries, pigments and coatings as well as dust

released during its mining and extraction. It is one of the 126 priority pollutants on the US

Environmental Agency’s list. It is a strong mutagen and carcinogen, as it causes transcription errors

and disables DNA repair activity, despite not being able to directly generate free radicals (Valko et

al. 2005).. Furthermore cadmium accumulates primarily in the gills of aquatic crustaceans such as

the test species, Gammarus pulex, causing issues with gas exchange, regulation of acidity and

osmoregulation, which are all crucial functions in these organisms (Felten et al. 2008).

In Denmark there are no dedicated regulations regarding mixture disposal of trace metals. The

Danish legislation on some heavy metals can be found in Bek. Nr. 1022. It differs between a general

and a short term exposure and between marine and freshwater. The highest concentration of a single

7

contaminant is defined after §§4-5, which roughly translates into: the limit of the particular

contaminant in water, sediment or biota that may not exceed a specified concentration in regard to

human health or environment. It is important to mention that the limit is on the total concentration

that is permitted to be discharged, and not the total concentration allowed in the water body. The

Danish environmental ministry can issue a more strict regulation for specific water bodies if they

deem them of special value. This also opens the possibility to implement new knowledge in the

field of mixture toxicity.

A more widely used approach for summing metals in a solution is the concentration addition model

(Borgman et al.2008). This model originates from the idea that the sum of the concentrations of a

trace metal mixture is the combined toxicity of the mixture. In legislation this is used in New

Zealand’s water quality guidelines (ANZECC and ARMCANZ, 2000). The drawback of this model

is that it assumes that toxicants only have an additive effect on the overall toxicity but as Norwood

et al. (2003: 805) states: “[..] 30% of the cases were more than additive, 43% were less than additive

and 27% were strict addition [..]” This was stated in regards to their review containing over 190

case- and experimental studies with mixed-metal toxicity. If the actual empirical toxicity of a

mixture is higher or lower than expected, the toxicants are described as having a synergistic or

antagonistic effect on each other (Norwood et al. 2003). As underlined by scientific research,

legislation has the possibility to adapt to more precise legislation formulation concerning mixture

contamination.

The Biodynamic Model

In this investigation the uptake rates of individual metals (zinc, copper and cadmium) are estimated,

and the focus is on the influence of Cu and Cd on Zn. To do this the biodynamic model is used,

which estimates the unidirectional flux of a metal or metalloid into an organism (Luoma and

Rainbow, 2005). In turn, one can use these values to see if the metals act competitively or non-

competitively when in the presence of one another.

This is the conceptual biodynamic model:

𝐴𝑐𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑒𝑑 𝑚𝑒𝑡𝑎𝑙 𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 =𝑤𝑎𝑡𝑒𝑟 𝑚𝑒𝑡𝑎𝑙 𝑢𝑝𝑡𝑎𝑘𝑒 + 𝑓𝑜𝑜𝑑 𝑚𝑒𝑡𝑎𝑙 𝑢𝑝𝑡𝑎𝑘𝑒

𝑙𝑜𝑠𝑠 𝑜𝑓 𝑚𝑒𝑡𝑎𝑙 + 𝑔𝑟𝑜𝑤𝑡𝑕 𝑜𝑓 𝑜𝑟𝑔𝑎𝑛𝑖𝑠𝑚

8

Where the water uptake is dependent on water concentrations of the metal and water uptake rate.

Food uptake depends on the assimilation efficiency (how well the metal assimilates with the

organism), the ingestion rate (i.e. how much food is being eaten per unit of time) and the metal

concentration in the food. Loss is the amount of metal lost (through means such as excretion) and

growth is the growth rate of the test organism.

The biodynamic model states that the steady-state concentration (Css, i.e. the accumulated

concentration) of a metal within an organism written as follows:

𝐶𝑠𝑠 =[(𝐾𝑢 × 𝐶𝑤) + (𝐴𝐸 × 𝐼𝑅 × 𝐶𝑓)]

𝑘𝑒 × 𝑔

Ku is the uptake rate of the metal ion from the exposure solution in which the organisms are

contained (µg×g-1

tissue×day-1

/µg Lwater-1

). Cw is the concentration of the metal or metalloid within the

solution (µg×L1). This part of the function can be summarized as IW (i.e. influx from water).

This project focuses only on the water uptake rate and the test organisms are therefore not getting

fed during the experiment. It is thus assumed that the food uptake (i.e.:𝐴𝐸 × 𝐼𝑅 × 𝐶𝑓) is close

enough to zero so that it can be ignored as a factor for the bioaccumulation. It is also assumed that

neither loss of metals (efflux, ke) nor growth (g) are factors, as the focus is on short-term metal

exposures (Luoma and Rainbow, 2005).

The model used for this project should therefore look as follows:

𝑀𝑒𝑡𝑎𝑙 𝑎𝑐𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑜𝑛 = 𝑊𝑎𝑡𝑒𝑟 𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 × 𝑊𝑎𝑡𝑒𝑟 𝑢𝑝𝑡𝑎𝑘𝑒 𝑟𝑎𝑡𝑒

𝑊𝑎𝑡𝑒𝑟 𝑢𝑝𝑡𝑎𝑘𝑒 𝑟𝑎𝑡𝑒 =𝑀𝑒𝑡𝑎𝑙 𝑎𝑐𝑐𝑢𝑚𝑖𝑙𝑎𝑡𝑖𝑜𝑛

𝑊𝑎𝑡𝑒𝑟 𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛

This model is used to analyze the data from the exposure experiment, from which the uptake rate

can be found as the linear relationship between the tissue burden and the water concentration.

9

Water Exposure Method

To answer this very specific question on uptake rates of zinc a water only exposure is the most

“clean” experimental setup. It is ideal since it can exclude the factors of feeding as mentioned in the

previous chapter and keeping the exposure medium uniform. This is done by making a moderately

hard freshwater mixture following the US EPA’s guide. Using formulated freshwater eliminates the

uncertainties natural stream water could include such as the presence of other metals, ions or

dissolved organic matter. The compounds used are ZnCl2, CuCl2 and CdCl2, all of which are

electrolytes, which when added into water dissolve into their ionic components.

According to the multi metal-gill binding model, Cd2+

binds readily to natural organic matter in

water, such as the ligands on a gill of an organism, binding stronger than both Zn2+

& Cu2+

when in

the presence of one another (Playle, 2004). This is another reason to use and organic content-free

exposure medium which could have affected the uptake rate.

By exposing the amphipods to the mixtures as well as the individual trace metals and determining

the uptake of each trace metal in the tissue of the organism with an AAS (Atomic Absorption

Spectrotometry) an estimate could be made as to whether the trace compete.

Test species

For a water only exposure, the native freshwater amphipod, Gammarus pulex was used. It is a

commonly used test species because of its ecological importance in leaf shredding feeding behavior

(Truhlar et al, 2014). This is a key function for the nutrient cycle and altering the substance

composition in streams, which classifies it as an ecosystem engineer (Lebrun et al, 2012). It is also a

common freshwater crustacean and is considered a good biomarker species for trace metals (Khan

et al. 2011). Furthermore it is an important food source for the higher trophic levels of the

freshwater food chain (Hickman et al, 2011).

Gammarus pulex has been a test species for many ecotoxicological experiments in the past. These

cover a vast range of experimental setups; however some are more relevant to our case. These range

from acidity response testing (Naylor et al. 1990) over pulse exposures to insecticides (Beketov et

al. 2008) to a wide variety of trace metal exposures (Charles et al. 2014, Strode et al. 2013, Khan et

al. 2012 (1,2)). A thorough evaluation undertaken by Maltby et al. (2002) states that Gammarus

10

pulex is a responsive and ecologically relevant species for sublethal water quality monitoring. This

and its ability to accumulate high amounts of zinc while withstanding its toxic effects (Spicer et al.

1998) together with its availability makes it a well suited organism for the acute mixed metal

exposure experiment.

Hypothesis

We expect the experimental results modelled according to the modified biodynamic model to give

insight into the competition of metals in mixtures. We expect the results to show that there is no

significant difference in the uptake rate of zinc in the presence of copper compared to singular zinc

as they do not compete for the same uptake channel.

In the zinc and cadmium mixture the ions are expected to exhibit competitive accumulation, i.e. the

uptake rate of zinc will be lower than in the single-zinc exposure, due to shared calcium uptake

channels.

The working questions, after taking our problem formulation into account, are as follows:

Is the uptake of zinc affected by the presence of copper and cadmium?

How do competitive and non-competitive metals affect the uptake rate of zinc?

How can the biodynamic model be applied to binary mixtures?

11

Method

Species collection

The Gammarus pulex were collected 4 weeks prior to the exposure experiment from a suitable

location from Hove å at 55.729526̊N, 12.204062E̊. This site was expected to be relatively

contamination-free (Københavns Amt. 1998). The organisms were collected with nets from the

stream bottom and were kept in buckets with stream water during transport to the 10.3ºC climate

room at Roskilde University. Four gammarids were frozen to determine background zinc, copper

and cadmium concentrations. In flat trays that resemble their shallow natural stream habitat with a

simulated water flow from the aeration the organisms were held and acclimatized to moderately

hard freshwater, formulated as seen in Figure 1.

Figure 1: Table 7 from the US EPA laboratory guidelines

The moderately hard water was formulated from this recipe and was interchanged with half of the

water in the trays every 24 hours except for the first day where a quarter was changed. After 4 days

the gammarid culture was only in moderately hard freshwater together with leaf litter from the

stream which acts as food source and to obviate cannibalism (McGarth, 2007).

Preliminary LC50 experiment

To optimize the results from the main exposure experiment, a series of preliminary experiments was

performed. To make the test species Gammarus pulex behave as naturally as possible in the

12

exposure experiment, a water depth of 2cm was found sufficient. Behavioral markers observed were

swimming, resting and mating performances. For the chosen size (100mL – radius of 2.8cm)

container the 2cm water depth resulted in working volumes of 50mL per exposure.

To find a sublethal concentration for the 3 trace metals used in the main uptake experiment, a 48

hour exposure was performed as a dose range finder in order to find the median lethal concentration

(LC50). An LC50 range finder (Median lethal concentration) was done in order to determine

suitable sub-lethal concentrations i.e. where the different metals were equitoxic, as a point of

reference for our trace metal exposure concentrations (Norwood et al. 2003).

A fixed ratio design was used, which enabled us to formulate metal mixtures which keep the metal

ratios constant when serially diluting and thereby maintaining their equitoxic doses e.g. at 40% of

the LC50 in all exposures each metal has equal toxicity. Additionally exposure concentrations

formulated in regards to the LC50 can ensure that all subjects in the 24 hour exposure are kept alive

while having a measurable uptake.

For the LC50 exposures, a study which found the 48 h LC50 of freshwater Gammarus pulex from

the Baltic region by Strode et al. 2013 was used in order to support our initial range. The stock

solutions were made from chloride salts of the three metals at 1g/L using the following calculations:

Table 1: Calculation for stock solutions

Molecula

r mass

Number

of H2O

Total

molecula

r mass

Mass of

metal

% of

metal

Mass of salt

per 1g metal

Mass in g of

salt in 100

mL solvent

CdCl2.2H2O 183.32 2 201.32 112.41 0.56 1.79 0.179

ZnCl2 136.32 0 136.32 65.38 0.48 2.09 0.209

CuCl2.2H2O 134.45 2 170.45 63.54 0.37 2.68 0.268

13

From this stock solution (1g/L) a working solution was created by diluting the stock. The cadmium

solution was diluted 100 times while copper and zinc were each diluted 10 times.

The following are the concentrations of the three metals used for our first rangefinder together with

the volumes needed to make these concentrations in 50ml.

Table 2: Concentrations and volumes of stock solution used for preliminary exposure

Zn Cd Cu

Concentration

(µg/L)

Volume needed

(mL)

Concentration

(µg/L)

Volume

needed (mL)

Concentration

(µg/L)

Volume

needed (mL)

400 0,2 10 0,05 200 0,1

500 0,25 20 0,1 300 0,15

600 0,3 30 0,15 400 0,2

700 0,35 40 0,2 500 0,25

800 0,4 50 0,25 600 0,3

900 0,45 700 0,35

1000 0,5 800 0,4

As mentioned before this was set up in 50mL beakers and the exposure lasted 48 hours. Each

beaker hosted five G. pulex. The exposure was carried out in formulated moderately hard

freshwater. No observable response (death) was noted in these exposures. The exposure was

therefore repeated; however this time the working solution was diluted so that the exposure was ten

times more concentrated compared to the previous. Again, the result was not significant.

Since the preliminary experiment did not show any clear results we investigated more literature.

Based on the collection of LC50s for G. pulex from other scientists’ publications, an LC50 for our

population was estimated upon which the exposure concentration range was based. The published

14

LC50s can be seen in the table below:

Table 3: A collection of LC50 values from relevant literature

Source Details Zn µg/L Cu µg/L Cd µg/L

Vellinger et al. 2012

(2) 48h x X 248,6

Lebrun et al. 2012 48h x X 63,2

Felten et al. 2008 96h x X 82,1

Strode et al. 2013 48h Adults 3100 550 98

96h Adults 680 360 13

48h Juveniles 700 470 22

96h Juveniles 390 180 5

Judy, 1979 96h 9-11mm x 190 X

Bat et al. 2000 96h 5200-1210 X X

96h 9-11mm x 30-80 X

MCLoughlin et al.

2000 96h 2510 X X

Güven et al. 1999 96h x 170 X

48h x 100 X

Stephenson. 1983 96h x 20 X

Taylor et al. 1991 96h 9-11mm X 37 X

15

Uptake experiment

The experimental design is constructed to examine how cadmium and copper affect the uptake of

zinc in binary mixtures. First the uptake of the three trace metals was investigated individually. The

concentration chosen to be the LC50 for this population, based on values from Table 3, were: zinc:

3000µg/L, cadmium: 200µg/L and copper: 1000µg/L. These values are based on three things: firstly

in this experiment a measurable metal uptake is needed. In practice that meant that the organisms

were exposed to a higher dose than the background contamination in the organism. However, the

concentration should not be so high that there is no uptake due to early fatality. Secondly the

preliminary experiment showed no death when exposed to 1000 µg/L zinc, 800 µg/L copper and 50

µg/L cadmium thus higher concentrations were needed than those in Table 3. Lastly, Table 3

showed that zinc was approximately three times higher than copper, and fifteen times higher than

that of cadmium resulting in the chosen equitoxic concentrations.

These values were used as an equitoxic dose. The organisms were exposed100, 80, 60, 40 and 20 %

of the LC50 in a 48 hour exposure for each metal. This dose range was used in a 24 hour exposure

for each of the trace metals and is considered sublethal.

The academic group structure used was to work sidelong with a master student in the experimental

phase of the project under the same supervisor. The experimental setup was therefore more

elaborate than needed to provide an answer our problem formulation. This was due to the master

student whose experimental aim was similar but not the same as ours. Because of this, we explain

an exposure setup and obtain data which is not mentioned in the results.

A stock solution was created using the method from the preliminary: 200mL of 100% LC50

concentration was created for each single, binary and tertiary metal exposure. Serial dilutions were

made from this stock solution to avoid propagating error. The volumes and concentrations can be

found in Appendix C. For binary and tertiary the same volume was made with each metal, which

represented 100% of their LC50 and then was serially diluted as in single exposures. The following

figure is a schematic overview of the exposure set up:

16

Figure 2: Experimental Setup

Schematic overview of the 24 hour experimental setup: the percentage refers to % of the LC50 for a 48

hour exposure. Each round symbol is a beaker with five gammarids. In the binary and tertiary beakers each

metal is present at the given % respectively.

Similar to the LC50 experiment 50mL beakers were used with five organisms in each and it was

judged from the preliminary experiment that aeration was not required. The exposure was carried

out over 24 hours in which the gammarids where not fed. As noted in the theory the lack of food

and the short exposure time is practiced to exclude uptake from food and the growth of the

organism. The beakers were soaked at the lowest metal concentration for 24 hours prior to the

exposure to prevent any loss of metal to the plastic surface for the actual exposure. Each gammarid

was assigned to an exposure randomly and the exposures maintained under constant temperature

(10.3̊C) in a climate room.

After exposure four organisms were chosen from each beaker for metal analysis. Deaths in the

treatments where noted and, if possible, used for uptake analysis. Between exposure and the AAS

(Atomic Absorption Spectroscopy) analysis each test organism (sample) was rinsed, labeled,

frozen, freeze-dried in pre-weighed eppendorfs and weighed to determine the dry weight. The

masses can be found in Appendix A.

17

Atomic Absorption Spectroscopy

The atomic absorption spectrometer determines quantitatively the amount of an element in a sample

by vaporizing it. Through this vapor a beam of light in an element-specific wavelength is sent and

the absorbed light is measured. A known amount of sample was vaporized and the spectrometer

analytically measured the concentration of the element - in this case the metals zinc, copper and

cadmium.

Analyzing the samples by AAS required liquefying the tissue. This was done by hot plate digestion

of the samples: each tissue sample was heated to 80̊C in its Eppendorf after addition of 02mL nitric

acid for 2 hours. These were weighted down on the heating plate to withstand the internal pressure

occurring from the reaction. When cooled to room temperature the liquid samples were transferred

to 5mL volumetric flasks and filled up with miliQ deionized water. The GFAAS/AAS matrix ratios

used were derived from the following volumes: 0,2uL HNO3 in 5mL miliQ water. As a control of

the digestion process 4 Eppendorfs without samples (acid only) where digested and also used in the

AAS analysis.

To translate the measured absorbance to metal concentration a standard curve is made with known

concentrations and measured absorbance is. This was used as a translation tool for the exposure

samples. The standard curves can be seen below.

18

Figure 3: Standard curve for zinc

Standard curve for zinc obtained by measuring the absorbance of known concentration of standard zinc

solution. The line is the best fit linear regression. The x-axis shows the nominal concentration of the

solution while the y-axis shows absorbance.

y = 0,7032x + 0,0298 R² = 0,9878

-0,100

0,000

0,100

0,200

0,300

0,400

0,500

0,600

0,700

0,800

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 1,1

Ab

sorb

ance

Known concentration [mg/L]

Standard curve zinc

19

Figure 4: Standard curve for copper

Standard curve for copper obtained by measuring the absorbance of known concentration of standard

solution. The line is the best fit linear regression. The x-axis shows the nominal concentration of the

solution while the y-axis shows absorbance.

y = 0,0287x + 0,0104 R² = 0,9993

0

0,1

0,2

0,3

0,4

0,5

0 2 4 6 8 10 12 14 16 18

Ab

sorb

ance

Known concentration [ug/L]

Standard curve copper

20

Figure 5: Standard curve for cadmium

Standard curve for cadmium obtained by measuring the absorbance of known concentration of standard

solution. The line is the best fit linear regression. The x-axis shows the nominal concentration of the

solution while the y-axis shows absorbance.

Most of the samples had to be diluted so that they were in the range measurable by the AAS as well

as fit on the standard curve. This dilution factor was found by trial and error with a few samples.

Dilution factors for the samples can be found in Appendix A. The varying dilution factors were due

to different tissue sample sizes. The blanks, controls and background were diluted less due to the

expectation of low absorbance.

A spreadsheet of the absorbance, sample weight and dilution factors can be found in Appendix A.

The equation of the standard curves were solved for x, and with that equation calculated the metal

concentration in the sample. This value was multiplied by the various dilution factors and the value

in microgram metal per gram tissue (dry weight was) obtained. After subtracting the background

concentration from unexposed gammarids, the values were plotted against the nominal metal

y = 0,5728x + 0,0105 R² = 0,9992

0

0,1

0,2

0,3

0,4

0,5

0 0,2 0,4 0,6 0,8 1

Ab

sorb

ance

Known concentration [ug/L]

Standard curve cadmium

21

concentrations in the exposures to visualize and calculate the uptake rate. A scatter plot with a best

fitted regression line was made for each single metal exposure and for zinc in the binary exposures.

The steepness of this regression line is the uptake rate constant, which is independent of exposure

concentration. The uptake rate constants are later used for comparison between exposures.

Statistical methods

The obtained data from the experiment was statistically valued. Furthermore, a statistical approach

was taken to investigate if the data significantly answered the hypothesis. Initially best fitted

regression lines were applied to the data which gave insight to how well the exposure concentration

(independent variable) correlated to the tissue burden measured (dependent variable). The R2

(Coefficient of determination) for these regression lines indicated how good the data fits the model

ranging from 0 to 1 (1 being a perfect fit). This value states how well the modified biodynamic

model fits the data. These values were firstly calculated in Excel and displayed together with a

scatterplot of the data. For a better statistical display these regressions were also performed in

Statistical Package for the Social Sciences (SPSS) to obtain more details on how well the data fits

the model and how significant this relation was. SPSS also allowed for comparison of the data

populations’ single zinc, zinc in mixture with copper and zinc in mixture with cadmium. This was

done by ANOVA (analysis of variance) which compared the mean and variance of one population

with that of means and variances of other populations. In this case the data population of single Zn

was compared to Zn in Cu mixture and Zn in Cd mixture respectably. Following the indications of

the binary mixture chapter this analysis should show that single Zn and Zn in Cu mixture are from

the same population (same uptake) and that single Zn and Zn in Cd mixture are from different

populations (different uptake).

Finally, if the data had significant value a comparison on the uptake rate constants (slopes) of the

three populations would have been carried out by ANCOVA (analysis of covariance) which

basically is a mix of ANOVA and linear regression. This test would have given us insight on

whether the categorical factor (No other metal, Cu or Cd being present in the solution) had a

statistically significant effect on slopes. This would indicate that the uptake rates of single Zn and

Zn in Cu mixture and Zn mixture were different or the same because of the presence of Cu or Cd.

This statistical background theory is supported by Samuels et al. 2012.

22

0102030405060708090

100

Co

ntr

ol

Zn 2

0%

Zn 4

0%

Zn 6

0%

Zn 8

0%

Zn 1

00

%

Cu

20

%

Cu

40

%

Cu

60

%

Cu

80

%

Cu

10

0%

Cd

20

%

Cd

40

%

Cd

60

%

Cd

80

%

Cd

10

0%

Zn+C

u 2

0%

Zn+C

u 4

0%

Zn+C

u 6

0%

Zn+C

u 8

0%

Zn+C

u 1

00

%

Zn+C

d 2

0%

Zn+C

d 4

0%

Zn+C

d 6

0%

Zn+C

d 8

0%

Zn+C

d 1

00

%

% M

ort

alit

y

Mortality

Results

The following are the results from the Atomic Absorbtion Spectrometer as well as mortality counts

conducted while collecting the test organisms for analysis after 24 exposure. The data is displayed

as scatter plots with fitted regression lines. The results also include statistical analyses such as the

ANOVA .

Mortality

The bar chart below shows the mortality that unexpectedly occurred during the uptake experiment.

It is displayed as a percentage of the total gammarids used in the metal analysis and plotted against

the contents of the beaker. The deaths occurr in mixtures containing copper, with as high as 100%

deaths in the highest single copper concentration.

Figure 6: Mortality

Mortality of Gammarus pulex in all the exposures after 24 hours. Number of replicates n=4.The x-axis

shows the contents and the LC50 percentage of the exposure while the y-axis shows mortality as a

percentage of the total number of replicates.

23

Single uptakes Figure 7: Single exposure zinc uptake

Uptake of zinc in the single 24h exposure. The points represent individual Gammarus pulex. A best fit

linear regression is fitted to the points with the equation and R2 value displayed. The x-axis shows nominal

exposure concentration while the y-axis displays tissue burden in µg/gdw.

The graph above illustrates tissue burden as a function of trace-metal concentration for singular

uptake, it shows a tendency to increase. Applying the altered bioaccumulation model from the

theory chapter to this data gives an uptake rate constant of 20.4 gdw/L/24h for zinc.

y = 20,43x + 70750 R² = 0,0172

-100000

0

100000

200000

300000

400000

500000

600000

0 600 1200 1800 2400 3000 3600

Tiss

ue

bu

rde

n [µ

g/gd

w]

Exposure concentration [µg/L]

Uptake single zinc

24

Figure 8: Single exposure copper uptake

Uptake of copper in the single 24h exposure. The points represent individual Gammarus pulex. A best fit

linear regression is fitted to the square points with the equation and R2 value displayed. The circular

orange points are not included in the regression line. The x-axis shows nominal exposure concentration

while the y-axis displays tissue burden in µg/gdw.

The graph above illustrates tissue burden as a function of concentration for copper uptake only, it

shows a tendency to increase up until 600µg/L where the curve flattens and begins to decrease. The

uptake rate constant is 0.62 gdw/L/24h.

y = 0,6164x - 18,724 R² = 0,8958

-100,0

100,0

300,0

500,0

0 200 400 600 800 1000 1200

Tiss

ue

bu

rde

n [µ

g/gd

w]

Exposure concentration [µg/L]

Uptake single copper

25

Figure 9: Single exposure cadmium uptake

Uptake of cadmium in the single 24h exposure. The points represent individual Gammarus pulex. A best fit

linear regression is fitted to the square points with the equation and R2 value displayed. The circular

orange points are not included in the regression line. The x-axis shows nominal exposure concentration

while the y-axis displays tissue burden in µg/gdw.

The graph above illustrates tissue burden as a function of trace metal concentration for single

cadmium uptake. It shows increasing uptake with increasing exposure concentration, until 120µg/L,

the trend flattens out. The uptake rate constant of cadmium is 0.15 gdw/L/24h.

y = 0,1529x + 1,3339 R² = 0,7941

-5

0

5

10

15

20

25

30

0 20 40 60 80 100 120 140 160 180 200

Tiss

ue

bu

rde

n [µ

g/gd

w]

Exposure concentration [µg/L]

Uptake single cadmium

26

Binary mixture uptake

Figure 10: Uptake of zinc in binary mixture with copper

Uptake of zinc in a binary mixture with copper after 24h. The points represent individual Gammarus pulex.

A best fit linear regression is fitted to the points with the equation and R2 value displayed. The x-axis

shows nominal exposure concentration while the y-axis displays tissue burden in µg/gdw.

The graph above illustrates tissue burden as a function of trace-metal concentration for zinc in

binary mixture with copper, it shows a general tendency to increase. The uptake rate constant is

18.3 gdw/L/24h.

y = 18,331x + 5595,4 R² = 0,0819

-50000

0

50000

100000

150000

200000

250000

0 600 1200 1800 2400 3000 3600

Tiss

ue

bu

rde

n [µ

g/gd

w]

Exposure concentration [µg/L]

Uptake of zinc in mixture with copper

27

Figure 11: Uptake of zinc in binary mixture with cadmium

Uptake of zinc in a binary mixture with cadmium after 24h. The points represent individual Gammarus

pulex. A best fit linear regression is fitted to the points with the equation and R2 value displayed. The x-axis

shows nominal exposure concentration while the y-axis displays tissue burden in µg/gdw.

The graph above illustrates tissue burden as a function of trace-metal concentration for zinc in

binary mixture with cadmium, and it shows a tendency to decrease when fitted with a regression

line. The uptake rate constant is -4.9 gdw/L/24h.

y = -4,9232x + 26696 R² = 0,0255

-15000

-5000

5000

15000

25000

35000

45000

55000

65000

75000

85000

95000

0 600 1200 1800 2400 3000 3600

Tiss

ue

bu

rde

n [

ug/

gdw

]

Exposure concentration [ug/L]

Uptake of zinc in mixture with cadmium

28

0

100000

200000

300000

400000

500000

600000

700000

800000

900000

1000000

0 20 40 60 80 100

Zin

c t

issu

e b

urd

en a

fter

24

h (

µg/

g (d

w)

% of LC50

Mean Zinc Tissue Burden

Zinc Zinc+Copper Zinc+Cadmium

Figure 12: Mean zinc tissue burden

Mean zinc tissue burdens of all Gammarus pulex in each exposure containing zinc. The mean was calculated

from the four available replicates (n=4). The error bars are plotted from the standard deviation of the

mean. The x-axis shows the nominal exposure concentration as a percentage of the LC50, while the y-axis

displays the mean zinc tissue burden after 24h of exposure in µg/gdw.

From the bar chart above, it can be seen that in the zinc and cadmium mixture, the uptake of zinc is

getting lower with higher concentration. In zinc and copper there appears to be a rather insignificant

trend with highest uptake at 20% and generally decreasing from there, which could be explained by

high copper mortality.

29

Figure 13: Uptake of copper in binary mixture with zinc

Uptake of copper in a binary mixture with zinc after 24h. The points represent individual Gammarus pulex.

A best fit linear regression is fitted to the points with the equation and R2 value displayed. The x-axis

shows nominal exposure concentration while the y-axis displays tissue burden in µg/gdw.

This result was found interesting due to the mortality in the zinc and copper mixture exposure. The

tissue burden shows increasing uptake in relation to exposure concentration until a flattening of the

uptake similar to the single Cu exposure.

Statistics

Linear Regression

The linear regression is done using the SPSS statistical software. In this case, the x value is the

concentration of the metal in µg/L and y is the value of accumulated metal in µg per gram dry

weight of G. pulex. The confidence level is set at 95.0%. Thus values for significance (the

probability that there is a linear relationship between the two variables), B (steepness of the slope or

the uptake rate), SE (standard error of the mean), and confidence intervals (upper and lower level at

y = 0,1508x + 228,12 R² = 0,0608

-100,0

100,0

300,0

500,0

700,0

900,0

1100,0

0 200 400 600 800 1000 1200

Tiss

ue

bu

rde

n [

ug/

gdw

]

Exposure concentration [ug/L]

Uptake of copper in mixture with zinc

30

95.0% confidence, the likelihood for the data to be within a certain area at 95.0% certainty) are

obtained. The standardized coefficient (Beta) is used to show the change in dependent variable

measured in standard deviations.

Table 4: Linear regression for singular zinc

Model Unstandardized

Coefficients

Standardized

Coefficients

Sig. 95,0% Confidence

Interval for B

B Std. Error Beta Lower

Bound

Upper

Bound

1(Constant) 70750,335 58810,819 0.242 ######## #######

VAR00003 20.430 33.663 0.131 0.550 -49.576 90.436

The table shows the linear regression for zinc, the uptake rate constant is modelled to 20.43

gdw/L/24h.The standard deviation is 33.7 and the confidence interval is between [-49.6:90.4].

Taking into account the high standard deviation and a wide confidence interval this is clearly a

varying set of data.

31

Table 5: Linear regression for singular copper

Model Unstandardized

Coefficients

Standardized

Coefficients

Sig. 95,0% Confidence

Interval for B

B Std. Error Beta Lower

Bound

Upper

Bound

1(Constant) -18.743 20.503 0.380 -63.869 26.383

VAR00001 0.616 0.063 0.946 0.000 0.477 0.756

The table shows linear regression for copper, the uptake rate constant of 0.62 gdw/L/24h. The

standard deviation is 0.1 and the confidence interval is between [0.5:0.8]. This is a much better fit

than in the previous regression.

Table 6: Linear regression for singular cadmium

Model Unstandardized

Coefficients

Standardized

Coefficients

Sig. 95,0% Confidence

Interval for B

B Std. Error Beta Lower

Bound

Upper

Bound

1(Constant) -18.743 20.503 0.380 -63.869 26.383

VAR00001 0.616 0.063 0.946 0.000 0.477 0.756

The table shows linear regression for Cd, the uptake rate constant 0.153 gdw/L/24h. The standard

deviation is 0.03 and the confidence interval is between [0.1:0.2]. Taking into account the small

standard deviation and narrow confidence interval this is a better fit than the two previous

regressions.

32

Table 7: Linear regression for zinc in mixture with copper

Model Unstandardized

Coefficients

Standardized

Coefficients

Sig. 95,0% Confidence

Interval for B

B Std. Error Beta Lower

Bound

Upper

Bound

(Constant) 5595.294 24788.621 0.824 ######## 57146.05

VAR00003 18.331 13.389 0.286 0.185 -9.513 46.175

The table shows linear regression for zinc uptake in mixture with copper, the uptake rate constant is

estimated to 18.3 gdw/L/24h. The standard deviation is 13.4 and the confidence interval is between

[-9.5:46.2]. As can be seen from the high standard deviation and wide confidence interval this is a

poor fit.

Table 8: Linear regression for zinc in mixture with copper

Model Unstandardized

Coefficients

Standardized

Coefficients

Sig. 95,0% Confidence

Interval for B

B Std. Error Beta Lower

Bound

Upper

Bound

(Constant) 26695.976 11780.261 0.034 2265.211 51126.742

VAR00003 -4.923 6.485 -0.160 0.456 -18.372 8.525

The table shows linear regression for zinc uptake in mixture with cadmium, the uptake rate constant

estimated to -4.9gdw/L/24h. The standard deviation is 6.5 and the confidence interval is between [-

18.4:8.5]. As can be seen from these values this is a bad fit.

33

One-way ANOVA

An ANOVA analysis is performed in order to see if any of the means and variances from the

groupings (Zn, Zn+Cu and Zn+Cd) vary majorly from one another. The null-hypothesis (H0) is that

the two populations have equal means and variances. The p value was chosen at 0.05.

H0= the two populations have equal means and variances. This would indicate that the presence of

other metals had an insignificant effect on the tissue burden of zinc.

H1= the two populations do not have equal means and variances. This would indicate that the

presence of other metals had a significant effect on the tissue burden of zinc.

The test assumes that the variance of the dependent variable is normally distributed; the test also

assumes that the samples are independent. The statistical software SPSS is used to perform this

analysis.

34

Table 9: ANOVA of the measured tissue burden values and metal exposure concentrations

Concentration Compared

uptake

Analysed

mixture

Significance

Sig <0,05 to

discard H0

Upper

confidence

interval

Lower

confidence

interval

600ug/L Zn Zn+Cu

Zn+Cd

0.930

0.797

-722587.3155

-490383.0655

555479.9837

787684.2337

1200 ug/L Zn Zn+Cu

Zn+Cd

0.993

0.994

-112462.4675

-112794.2175

122207.3064

121875.5594

1800 ug/L Zn Zn+Cu

Zn+Cd

0.323

0.377

-131052.7084

-143704.7084

449080.9125

436428.9125

2400 ug/L Zn Zn+Cu

Zn+Cd

0.997

0.547

-160697.4203

-101081.1703

169679.7137

229295.9637

3000 ug/L Zn Zn+Cu

Zn+Cd

0.329

0.236

-279834.8206

-226089.5706

945039.0715

998784.3215

These results show singular zinc at a certain concentration compared to zinc in mixtures and how

significantly these tissue burdens differ from each other at these concentrations. The difference is

not significant in any of the exposure concentrations, which is expected in zinc and copper but not

in zinc and cadmium.

According to the analysis, both copper and cadmium have an insignificant effect on the tissue

burden and therefore uptake, of zinc. The insignificance of these results led to a focus on the uptake

rate constants obtained from the exposures.

35

Discussion

A measurable uptake is noted, which displays large variance. Some of the variation can be due to

physiological intraspecies differences such as sex, size and lifestage (Geffard et al. 2007, Pastorinho

et al. 2009). Others can be attributed to the level of accuracy of the analytical method used.

Especially the zinc data has a wide distribution of absorbances across each concentration. Negative

tissue burdens are due to the calculated standard curve which in all exposure cases has a y-intersect

higher than some of the absorbance values, causing a negative calculated uptake.

Mortality

An unexpected result obtained from the experiment is the mortality of the test organisms during the

exposure. As the results show this is only a factor in any mixtures containing copper and is as high

as 100% in the highest concentration of singular copper. It is also an issue in the zinc-copper mixed

exposure, however there the mortality only reached 25% in the following concentrations: 20% and

80% LC50. The deaths occurring in the single Cu exposures are spread across the concentration

range with high percentage mortality in the higher end (with the exception of 60%) and some deaths

in the lower concentration. This could suggest that the LC50 based on the literature was too high for

tests involving sublethal concentrations for this population. Lebrun et al (2012) also suggest that G.

pulex have a high susceptibility to copper.

These deaths could have affected the measured uptake as dead organisms do not actively take up

substances from their environment and since some deaths also occurred in the binary mixtures, this

could have affected the uptake of zinc as well as copper. Since no deaths are observed in the

controls the conditions of the setup appear to be suitable for the test organism.

Statistics

According to the linear regression, there is only an indication of a statistically significant correlation

between the data points and the linear function for singular cadmium. According to the ANOVA

test, the presence of cadmium and copper shows no statistically significant effect on the uptake of

zinc at any of the concentrations, which could indicate that the metals do not share uptake channels.

In the case of a statistical difference the regression lines would as noted be compared by ANCOVA

but in this case they are compared visually since there is no statistical proof of them being different

36

from each other.

Based on the ANOVA test there is no statistical evidence that neither copper nor cadmium affect the

uptake of zinc. This means it is not statistically certain whether the metals compete or not.

However, a graphic interpretation of the uptake rate constant (slope) indicates a similar uptake for

singular zinc and binary mixture zinc and copper. This would suggest no competition. The graphic

interpretation also provides insights on zinc in binary mixture with cadmium as the tendency in

uptake is decreasing. This suggests that the metals are competing. Even though there is no statistical

proof for our hypothesis, one argument can be based on the graphic interpretation, and more

precisely the direction and angle of the slope.

Zinc

The tissue concentrations of zinc display a wide variability in zinc uptake, as seen in Figure 7, 10

and 11. Since no deaths were observed (Fig. 6) in the single zinc exposure the concentrations used

were indeed sublethal. Some of the data points which show a Zn tissue burden of 0 are probably due

to measuring or dilution errors since other exposure experiments with lower concentrations show

uptake (Khan et al. 2011). Despite some curiously high tissue burdens in some of the lower

concentrations the data was not manipulated (by removing outliers) before calculating the uptake

rate constant or uptake rate. This is because the aim of the project does not encompass thorough

theory of the physiological aspects of metal accumulation (Khan et al. 2011, Pastorinho et al. 2009,

Valko et al. 2005).

The zinc uptake rate constants are calculated in the different exposure scenarios as the best fitted

linear regression of the tissue burden versus water concentration. In single exposure the uptake rate

constant is 20.43gdw/L/24h and is displayed in Figure 7. This seems to be high compared to similar

exposure experiments (Pastorinho et al. 2009). The R2 of the fitted line for single zinc exposure is

0.02 and for binary mixtures with Cu and Cd the values are 0.08 and 0.03 respectively. This shows

that the data is not fitting the model (line) but nevertheless these lines would be compared as if the

data would have fitted. From a statistical point of view if zinc and zinc+copper are from the same

statistical population it would suggest there to be no competition between the two, as expected from

the theory which supports a part of the hypothesis. Single Zn uptake does not differ statistically

from Zn in mixture with Cd which probably is due to the data varying so much. When comparing

37

the uptake rate constant of single Zn (20.4gdw/L/24h) with Zn in Cu mixture (18.3gdw/L/24h) they

are almost equal as hypothesized. When comparing the uptake rate constant in single Zn with that

of Zn in Cd mixture (-4.9gdw/L/24h) they are very different. In presence of Cd the uptake rate

constant of Zn is basically zero since a decreasing relationship between Zn tissue burden and

increasing exposure concentration seems unrealistic. The low uptake is supported by Poteat et al

(2012) who found that the presence of Cd reduced the uptake of Zn by 58% in a similar exposure

experiment. The low uptake supports the hypothesis that Cd would use the same uptake channel and

thereby have a competitive effect on Zn uptake. Lastly, when investigating the mean single zinc

tissue burden in Figure 12 there is an indication that the presence of high cadmium concentrations

lowers the uptake of zinc. This could indicate competition in uptake channels. Cd seems to

completely saturate the binding sites (Rainbow. 2007) of the Ca2+

uptake channel ultimately

blocking the uptake of Zn.

A curious result in the binary Zn and Cu exposure is that the mortality (Figure 6), this is believed to

be due to Cu (Lebrun et al. 2012), is lower than in the single Cu exposure. By comparing the

distribution of the data in Figure 8 with that of Figure 13, it is apparent that zinc has no effect on the

uptake of copper in the organism. This means that Zn in other ways is antagonistic to the toxic

effect of Cu. Other influences on the toxicity than the competition of uptake channels by

bioavailable Cu may be explained physiologically (Khan et al. 2011, Pastorinho et al. 2009).

Copper

Copper results are displayed in Figure 8. An increasing uptake can be seen when increasing the

exposure concentration for the lower concentrations (0-400 µg/L) which is as expected. In higher

concentrations the binding sites for Cu might be saturated (Luoma and Rainbow. 2005, Lebrun et al.

2012). The organism therefore cannot take up more Cu even if the concentration in the water

increases. When comparing the two highest concentrations (600 and 1000 µg/L) with Figure 6 it

seems plausible to conclude that the organisms’ death resulted in a low tissue burden. This fits

perfectly with the theory that the metals are taken up by water passing through the gills (Luoma and

Rainbow. 2005) which is an active process and does not proceed after death. Taking the influence of

these deaths into account it is plausible to conclude that at higher concentrations saturation of the

binding sites occurs (Luoma and Rainbow, 2005, Lebrun et al. 2012).

38

Cadmium

The cadmium results are displayed in Figure 9 and show a similar uptake pattern as in copper. The

tissue burden is proportional to the concentration in the lower concentrations (0-80µg/L) which also

has little concentration variation. At higher concentrations the tissue burden is not increasing with

increasing exposure concentration indicating that the binding sites in the organism are saturated

(Luoma and Rainbow. 2005, Lebrun et al. 2012). As there is no mortality in Cd single exposure this

gives a clearer view on the saturation than Cu. When comparing the uptake rate for this 24 hour

exposure to a longer exposure by Vellinger et al. 2012(2) the observed uptake rates seem plausible.

Controls

The control gammarids all have low tissue burdens arguing that the uptake observed in the

exposures indeed came from the metal added. This indicates that the setup was done correctly.

Controls have also been used as 0 concentrations in figures 7-13 and with the exception of one zinc

analysis they are as expected.

Background tissue concentration values have been used to correct for the metal already in the

gammarids at the time of collection and to give an idea of how “clean” the population is. All the

metal values for these samples are relatively low, compared to natural accumulated metals in G.

pulex collected in Scotland (Rainbow and Moore. 1986). This was as expected from the stream

which is not attached to any pollution sources (Københavns Amt, 1998) and makes the population

suitable for the exposure set up.

Blanks (empty eppendorf tubes digested and analyzed with the AAS) show close to zero absorbance

values which suggest that the digestion method did not contaminate the samples and therefore any

metal ions must have come from the exposure (Appendix A).

Biodynamic model

At the point (concentration) where the uptake rate of cadmium is saturated (around 60% LC 50,

Figure 9) no uptake of zinc can be seen in the mixture (Figure 12). This indicates that at this

concentration Cd saturates the binding sites of the organism. It also indicates that at lower

concentrations, these two metals can be taken up simultaneously. Therefore by applying the

39

biodynamic model it is possible to gauge if and when metals in mixture have an effect on each

other’s uptake.

The biodynamic model that is used to calculate the uptake rate is not directly taking into account the

effects of other substances in the solution. We suggest that this model could be enhanced to predict

uptake in mixtures. This would result in a complex Ku that would describe the competition on the

binding sites, thereby considering the effect mentioned in the binary mixtures chapter.

However, because of the indication that the competitive behavior starts at a certain concentration, it

is not valid just to find the difference in uptake rate between single zinc and zinc in mixture and

then proclaim that the difference is the inhibiting effect of the other metal. On the other hand, by

comparing the data from experiments like this, one can predict uptake interactions between metals

at different concentrations (Daka et al. 2006).

Bias

The exposure setup was well planned and was carried out without any notable incidents. Five weeks

of intensive work was performed to get acknowledgeable absorbance values from the AAS. This

was due to varying standard curves and measured absorbance. We countered this by getting new

lamps for the AAS and help of a laboratory technician. In the end the time pressure prohibited any

further attempts to get clearer results.

Relevance

The ecological relevance of our study lies in the fact that in real life water bodies, organisms such

as the Gammarus pulex are exposed to a vast variety of toxicants as opposed to one, which is what

traditional laboratory tests use. The use of several metals allows researchers to create a real life

scenario which can bring results which give us a better understanding the effect of man-made

pollution on organisms in natural environments.

Furthermore our study focuses on sublethal accumulation of trace metals in the tissues of the

organism. This is closer to a natural environment scenario than an acute toxicity testing as it uses

sublethal concentrations. Observations from natural water bodies have shown that sublethal

concentrations are more relevant than high acute ones (Khan et al. 2011). .

40

Besides broadening our knowledge and understanding of the issue, this could allow legislative

powers to make more ecologically relevant disposal regulations for these metals in solution. The

Danish environmental ministry can issue a more strict regulation for specific water bodies if they

deem them of special value. Research of this type can aid organizations such as the Danish

environmental ministry to issue regulations more relevant to the issue at hand.

Conclusion

The designed experimental setup sponsored the results it was designed to give. Mortality was rare

and originated from overdosing copper exposures but the data indicates that the presence of zinc has

an antagonistic effect on Cu toxicity. Data obtained was not deemed statistically significant when

analyzed with linear regression and ANOVA which is due the variability of the zinc data. In the

singular uptake exposures we noted that 600µg/L Cu concentration and 120ug/L cadmium

concentration seemed to saturate the binding sites in Gammarus pulex leaving higher exposure

concentrations without any further increase in uptake. In singular Zn the ANOVA showed that the

uptake of Zn and Zn in mixture with Cu is the same (low significance of 0.329-0.997) which was

supported by the poorly fitting regression line (r2 of 0.017 and 0.082 respectively) comparison of

these two exposures. This suggests a very similar uptake rate. Copper reaching saturation

concentration while having no effect on zinc uptake, also suggests that the two ions use different

uptake channels. Cd, on the other hand, also did not influence the uptake of Zn statistically (sig.

0.23-0.97), but the linear regression indicated together with mean zinc tissue burden data that Cd in

fact is inhibiting the uptake of Zn in higher concentrations, ultimately reducing the uptake rate

constant of zinc to zero, suggesting that they use the same uptake channel. The biodynamic model

can be used to estimate single metal uptake rates even in mixture experiments but adjustments are

needed to predict these uptake rates since it is not accounting for competition. In conclusion, the

uptake rate of Zn was not changed in the presence of Cu but high Cd concentrations reduced and

ultimately blocked the uptake of Zn in the test species. These results indicated that the hypothesis is

correct and thereby answered the problem formulation.

41

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47

Appendices

Appendix A: AAS Results

Table 1: Zinc Raw Data

Treatment Concentration

(% LC50)

Gammarid

(g(dw))

Dilution

factor

Absorbance Tissue

Burden

(mg/g

(dw))

Tissue

Burden -

average

background

(mg/g (dw))

Background 0 0,0186 10 0,1218 351,6949 23,17115

Background 0 0,0163 10 0,0998 305,3526 -23,1711

Background 0 0,0161 1000 0,0074 -9892,66 -10221,2

Background 0 0,0057 500 0,019 -6736,12 -7064,65

Control 0 0,0189 1000 0,0025 -10270,5 -10599

Control 0 0,0113 500 0,026 -1195,55 -1524,07

Control 0 0,0222 10 0,1271 311,6384 -16,8853

Control 0 0,0274 500 0,3047 35668,54 35340,02

Zn 20 0,012 2500 0,2122 270193,4 269864,9

Zn 20 0,0077 1000 0,001 -26594,6 -26923,1

Zn 20 0,0138 500 0,0026 -7007,3 -7335,83

48

Zn 20 0,0112 2500 0,3458 501533,8 501205,3

Zn 40 0,0163 10 0,0837 235,1215 -93,4022

Zn 40 0,0141 1000 0,3337 153250,8 152922,3

Zn 40 0,0152 1000 0,1088 36955,12 36626,6

Zn 40 0,0189 500 0,0073 -4232,35 -4560,88

Zn 60 0,0114 10 0,1157 535,7713 207,2476

Zn 60 0,0117 500 0,181 45943,82 45615,29

Zn 60 0,018 500 0,296 52577,11 52248,58

Zn 60 0,0166 2500 0,5317 537452,7 537124,2

Zn 80 0,0171 500 0,7357 146760,2 146431,7

Zn 80 0,0158 1000 0,3087 125511,2 125182,7

Zn 80 0,0225 500 0,3239 46470,1 46141,58

Zn 80 0,0136 10 0,0696 208,0824 -120,441

Zn 100 0,0182 500 0,255 43990,42 43661,9

Zn 100 0,017 500 0,245 45004,35 44675,83

Zn 100 0,017 2500 0,3245 308150,1 307821,6

Zn 100 0,0086 5000 0,3109 1162047 1161718

49

Zn + Cu 20 0,0185 10 0,1375 413,9378 85,41411

Zn + Cu 20 0,0048 2500 0,3042 1016188 1015859

Zn + Cu 20 0,0123 500 0,2189 54657,22 54328,7

Zn + Cu 20 0,0083 10 0,156 1081,116 752,5926

Zn + Cu 40 0,0175 1000 0,3088 113359,3 113030,8

Zn + Cu 40 0,0289 10 0,26 566,3679 237,8442

Zn + Cu 40 0,0094 10 0,1011 539,3278 210,8041

Zn + Cu 40 0,0179 500 0,2929 52255,13 51926,61

Zn + Cu 60 0,0122 10 0,0612 183,0042 -145,52

Zn + Cu 60 0,0173 10 0,0832 219,4756 -109,048

Zn + Cu 60 0,0242 10 0,0801 147,7896 -180,734

Zn + Cu 60 0,0181 10 0,005 -97,4236 -425,947

Zn + Cu 80 0,0229 10 0,174 447,7349 119,2112

Zn + Cu 80 0,0211 500 0,2695 40387,48 40058,95

Zn + Cu 80 0,0269 10 0,3592 870,6878 542,1641

Zn + Cu 80 0,0186 2500 0,3011 259279,4 258950,9

Zn + Cu 100 0,0183 500 0,2249 37902,45 37573,93

50

Zn + Cu 100 0,0181 10 0,1767 577,0778 248,5541

Zn + Cu 100 0,0181 1000 0,5122 189504,6 189176,1

Zn + Cu 100 0,0185 10 0,2376 798,6656 470,1418

Zn + Cd 20 0,015 10 0,0799 237,4858 -91,0379

Zn + Cd 20 0,0097 500 0,2728 89062,66 88734,14

Zn + Cd 20 0,0162 500 0,2763 54095,74 53767,21

Zn + Cd 20 0,0212 10 0,0676 126,7789 -201,745

Zn + Cd 40 0,0237 500 0,5876 83674,15 83345,63

Zn + Cd 40 0,0139 10 0,0704 207,6837 -120,84

Zn + Cd 40 0,0142 10 0,1115 409,0956 80,57193

Zn + Cd 40 0,0256 500 0,6329 83754,96 83426,43

Zn + Cd 60 0,0108 10 0,112 541,1768 212,6531

Zn + Cd 60 0,0136 10 0,1422 587,6497 259,126

Zn + Cd 60 0,0083 10 0,0338 34,26676 -294,257

Zn + Cd 60 0,0198 500 0,3077 49898,16 49569,63

Zn + Cd 80 0,0163 500 0,3108 61288,62 60960,1

Zn + Cd 80 0,0203 10 0,0803 176,8832 -151,641

51

Zn + Cd 80 0,0257 10 0,087 158,2538 -170,27

Zn + Cd 80 0,0196 10 0,2768 896,0495 567,5258

Zn + Cd 100 0,0205 500 0,1371 18608,31 18279,79

Zn + Cd 100 0,02 500 0,0008 -5155,01 -5483,53

Zn + Cd 100 0,0209 10 0,0969 228,2797 -100,244

Zn + Cd 100 0,0247 10 0,0717 120,6169 -207,907

Blank 0 -0,0005 10 0,2054

Blank 0 -0,0009 10 0,2039

Blank 0 0,019 10 0,2007

Blank 0 0,0003 10 0,1652

52

Table 2: Copper Raw Data

Treatment Concentration

(% LC50)

Gammarid

(g (dw))

Dilution

factor

Absorbance Tissue

Burden

ug/g

(dw)

Tissue Burden -

average

background(mg/g

(dw))

Background 0 0,0186 2 0,9844 21,55 -55,32

Background 0 0,0163 8 0,9094 90,34 13,48

Background 0 0,0161 8 0,9378 94,53 17,67

Background 0 0,0057 4 0,725 101,04 24,17

Control 0 0,0189 8 1,0887 94,41 17,55

Control 0 0,0113 4 0,7683 54,30 -22,56

Control 0 0,0222 8 1,0715 79,03 2,17

Control 0 0,0274 8 1,0438 62,27 -14,59

Cu 20 0,0166 100 0,1899 188,38 111,52

Cu 20 0,0166 100 0,1791 177,05 100,19

Cu 20 0,0164 100 0,1544 152,97 76,11

Cu 20 0,0162 100 0,1429 142,49 65,63

Cu 40 0,024 100 0,224 155,05 78,19

Cu 40 0,0061 100 0,1203 313,87 237,01

53

Cu 40 0,0102 100 0,166 265,76 188,90

Cu 40 0,008 100 0,1443 291,59 214,73

Cu 60 0,0184 100 0,5893 548,12 471,26

Cu 60 0,0217 100 0,1806 136,64 59,78

Cu 60 0,0161 100 0,1581 159,82 82,96

Cu 60 0,0064 100 0,1419 357,96 281,10

Cu 80 0,0135 100 0,1904 232,29 155,43

Cu 80 0,0162 100 0,1483 148,30 71,44

Cu 80 0,0125 100 0,1298 166,41 89,55

Cu 80 0,0057 100 0,124 347,21 270,35

Cu 100 0,0205 100 0,1879 150,85 73,98

Cu 100 0,0117 100 0,1434 198,04 121,18

Cu 100 0,018 100 0,1913 175,09 98,22

Cu 100 0,0105 100 0,1447 222,83 145,97

Zn + Cu 20 0,0185 100 0,3528 322,44 245,58

Zn + Cu 20 0,0048 100 0,3508 1235,8 1158,62

Zn + Cu 20 0,0123 100 0,4029 555,93 479,07

54

Zn + Cu 20 0,0083 100 0,3457 703,79 626,93

Zn + Cu 40 0,0175 100 0,4435 431,16 354,30

Zn + Cu 40 0,0289 100 0,5066 299,12 222,26

Zn + Cu 40 0,0094 100 0,3261 585,11 508,24

Zn + Cu 40 0,0179 100 0,5489 524,11 447,25

Zn + Cu 60 0,0122 100 0,6886 968,47 891,61

Zn + Cu 60 0,0173 100 0,3551 347,12 270,26

Zn + Cu 60 0,0242 100 0,4309 302,72 225,86

Zn + Cu 60 0,0181 100 0,4273 401,27 324,41

Zn + Cu 80 0,0229 100 0,3995 296,02 219,15

Zn + Cu 80 0,0211 100 0,5591 453,04 376,18

Zn + Cu 80 0,0269 100 0,4163 262,88 186,02

Zn + Cu 80 0,0186 100 0,4057 370,26 293,39

Zn + Cu 100 0,0183 100 0,2934 269,42 192,55

Zn + Cu 100 0,0181 100 0,571 539,59 462,73

Zn + Cu 100 0,0181 100 0,3729 348,91 272,05

Zn + Cu 100 0,0185 100 0,4947 456,07 379,21

55

Cu + Cd 20 0,0174 100 0,4862 476,39 399,53

Cu + Cd 20 0,0163 100 0,3342 346,08 269,22

Cu + Cd 20 0,0101 100 0,2667 442,09 365,23

Cu + Cd 20 0,0105 100 0,2028 319,23 242,37

Cu + Cd 40 0,0091 100 0,7066 1332,5 1255,99

Cu + Cd 40 0,0245 -76,86

Cu + Cd 40 0,007 100 0,6425 1573,17 1496,31

Cu + Cd 40 0,0138 100 0,3244 396,40 319,54

Cu + Cd 60 0,0144 100 0,3987 469,78 392,92

Cu + Cd 60 0,0117 100 0,32 461,00 384,14

Cu + Cd 60 0,0716 100 0,3056 71,83 -5,03

Cu + Cd 60 0,0033 100 0,1904 950,27 873,41

Cu + Cd 80 0,0188 100 0,3263 292,74 215,88

Cu + Cd 80 0,0167 100 0,3472 351,35 274,49

Cu + Cd 80 0,0169 100 0,2844 282,46 205,59

Cu + Cd 80 0,0138 100 0,4962 613,29 536,43

Cu + Cd 100 0,0199 100 0,4048 345,28 268,42

56

Cu + Cd 100 0,0241 100 0,4977 352,26 275,40

Cu + Cd 100 0,0217 100 0,5137 404,07 327,21

Cu + Cd 100 0,0162 100 0,3705 387,25 310,39

Blank 0 -0,0005 2

Blank 0 -0,0009 2

Blank 0 0,019 2

Blank 0 0,0003 2

57

Table 3: Cadmium Raw Data

Treatment Concentration

(% LC50)

Gammarid

(g (dw))

Dilution

factor

Absorbance Tissue

burden

ug/g (dw)

Tissue burden-

average

background

ug/g (dw)

Background 0 0,0186 100 0,1848 -1,80029

Background 0 0,0163 10 0,0283 0,779924 -1,02037

Background 0 0,0161 10 0,1287 4,106647 2,306357

Background 0 0,0057 10 0,0178 1,250467 -0,54982

Control 0 0,0189 10 0,0355 0,875267 -0,92502

Control 0 0,0113 10 0,0225 0,852005 -0,94828

Control 0 0,0222 10 0,0893 2,034215 0,233925

Control 0 0,0274 10 0,1382 2,597453 0,797163

Cd 20 0,0165 100 0,0393 11,25081 9,450516

Cd 20 0,0275 100 0,0702 12,72727 10,92698

Cd 20 0,0206 100 0,0064 0,516422 -1,28387

Cd 20 0,0135 100 0,186 71,5524 69,75211

Cd 40 0,0154 100 0,0568 18,09892 16,29863

Cd 40 0,0104 100 0,0671 32,06833 30,26804

58

Cd 40 0,0194 100 0,0622 15,84777 14,04748

Cd 40 0,0141 100 0,0567 19,72989 17,9296

Cd 60 0,0057 100 0,0316 25,38261 23,58232

Cd 60 0,008 100 0,035 20,34574 18,54545

Cd 60 0,023 100 0,0556 11,84089 10,0406

Cd 60 0,018 100 0,0836 3,544964 1,744674

Cd 80 0,015 100 0,0967 5,016294 3,216004

Cd 80 0,0133 100 0,0459 16,59734 14,79705

Cd 80 0,0073 100 0,0354 22,58817 20,78788

Cd 80 0,0171 100 0,0699 20,37452 18,57423

Cd 100 0,0159 100 0,1925 62,92654 61,12625

Cd 100 0,0074 100 0,04 25,58942 23,78913

Cd 100 0,0173 100 0,2552 77,11229 75,312

Cd 100 0,0121 100 0,0439 17,36416 15,56387

Zn + Cd 20 0,015 100 0,0795 26,63121 24,83092

Zn + Cd 20 0,0097 100 0,04 19,52182 17,72153

Zn + Cd 20 0,0162 100 0,0506 15,16942 13,36913

59

Zn + Cd 20 0,0212 100 0,1488 36,23043 34,43014

Zn + Cd 40 0,0237 100 0,1167 25,20424 23,40395

Zn + Cd 40 0,0139 100 0,0931 33,94306 32,14277

Zn + Cd 40 0,0142 100 0,034 11,0878 9,287513

Zn + Cd 40 0,0256 100 0,1143 22,83494 21,03465

Zn + Cd 60 0,0108 100 0,0702 32,40741 30,60712

Zn + Cd 60 0,0136 100 0,1009 37,74249 35,9422

Zn + Cd 60 0,0083 100 0,0435 25,05768 23,25739

Zn + Cd 60 0,0198 100 0,1348 35,03116 33,23087

Zn + Cd 80 0,0163 100 0,1052 32,89388 31,09359

Zn + Cd 80 0,0203 100 0,0749 2,769216 0,968926

Zn + Cd 80 0,0257 100 0,102 3,107814 1,307524

Zn + Cd 80 0,0196 100 0,0622 2,30251 0,50222

Zn + Cd 100 0,0205 100 0,1056 4,049428 2,249138

Zn + Cd 100 0,02 100 0,0957 3,718575 1,918285

Zn + Cd 100 0,0209 100 0,1165 4,427174 2,626884

Zn + Cd 100 0,0247 100 0,1435 4,700258 2,899968

60

Cu + Cd 20 0,0174 100 0,0606 2,513364 0,713074

Cu + Cd 20 0,0163 100 0,0497 2,099256 0,298966

Cu + Cd 20 0,0101 100 0,0293 1,624813 -0,17548

Cu + Cd 20 0,0105 100 0,022 0,956039 -0,84425

Cu + Cd 40 0,0091 100 0,0314 2,004804 0,204514

Cu + Cd 40 0,0245 100 -1,80029

Cu + Cd 40 0,007 100 0,0393 3,591381 1,791091

Cu + Cd 40 0,0138 100 0,0472 2,321421 0,521131

Cu + Cd 60 0,0144 100 0,0891 4,764607 2,964317

Cu + Cd 60 0,0117 100 0,0563 3,417013 1,616723

Cu + Cd 60 0,0716 100 0,0654 0,669308 -1,13098

Cu + Cd 60 0,0033 100 0,025 3,835492 2,035202

Cu + Cd 80 0,0188 100 0,1343 5,748172 3,947882

Cu + Cd 80 0,0167 100 0,1759 8,645419 6,845129

Cu + Cd 80 0,0169 100 0,1037 4,81389 3,0136

Cu + Cd 80 0,0138 100 0,0805 4,427779 2,627489

Cu + Cd 100 0,0199 100 0,1761 7,263973 5,463684

61

Cu + Cd 100 0,0241 100 0,2749 9,576601 7,776311

Cu + Cd 100 0,0217 100 0,2207 8,455513 6,655223

Cu + Cd 100 0,0162 100 0,1546 7,764544 5,964254

Blank 0 -0,0005 0 0,0074

Blank 0 -0,0009 0 0,0016

Blank 0 0,019 0 0,002

Blank 0 0,0003 0 0,0073

62

Appendix B: Mean Zinc Tissue Burden

Sample contents

Mean Zinc Tissue

Burden (mg/g (dw))

Standard Deviation

Background contamination 328,5 32,76895

Control 6128,5 20240,38

20% Zinc LC50 184531,3 251057,6

40% Zinc LC50 46552,2 73487,04

60% Zinc LC50 159127,4 253275,1

80% Zinc LC50 79737,4 68359,15

100% Zinc LC50 389797,9 529622,4

20% Zinc + Copper LC50 268085,0 499382,4

40% Zinc + Copper LC50 41680,0 53642,79

60% Zinc + Copper LC50 113,2 143,4409

80% Zinc + Copper LC50 75246,3 124110

100% Zinc + Copper LC50 57195,7 89933,64

20% Zinc + Cadmium LC50 35880,7 43622,97

40% Zinc + Cadmium LC50 42011,5 48154,65

60% Zinc + Cadmium LC50 12765,3 24756,5

80% Zinc + Cadmium LC50 15630,0 30441,05

63

100% Zinc + Cadmium LC50 3450,6 10412,89

64

Appendix C: Solution concentrations

The solution concentrations are noted for each exposure and made from the same stock solution.

These working solutions where then serial diluted to the concentrations seen in the table.

Singular Zn solution of 3000ug/L

% of LC50 Zn

concentration

Ug/L

mL solution mL water

100 3000 50 0

80 2400 40 10

60 1800 30 20

40 1200 20 30

20 600 10 40

0 0 0 50

Singular Cu solution of 1000ug/L

100 1000 50 0

80 800 40 10

60 600 30 20

40 400 20 30

20 200 10 40

65

0 0 0 50

Singular Cd solution of 200ug/L

100 200 50 0

80 160 40 10

60 120 30 20

40 80 20 30

20 40 10 40

0 0 0 50

Binary Zn+Cu solution 3000ug/L Zn & 1000ug/L Cu

100 3000/1000 50 0

80 2400/800 40 10

60 1800/600 30 20

40 1200/400 20 30

20 600/200 10 40

0 0/0 0 50

Binary Zn+Cd solution 3000ug/L Zn & 200ug/L Cd

100 3000/200 50 0

80 2400/160 40 10

66

60 1800/120 30 20

40 1200/80 20 30

20 600/40 10 40

0 0 0 50